library(swimplot) library(pheatmap) library(reshape2) library(coxphf) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(broom) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(ComplexHeatmap) library(tidyverse) library(readxl) library(survival) library(janitor) library(openxlsx) library(writexl) library(rms) library(DT)

#ctDNA Detection rate by Stage and Window

#MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.Surveillance %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Anytime post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.anytime %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.anytime == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.anytime, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.anytime == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA MRD Detection rate Stage I/II vs III

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"))
circ_data$Stage_Grouped <- factor(ifelse(circ_data$Stage %in% c("I", "II"), "I/II", "III"))
contingency_table <- table(circ_data$Stage_Grouped, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(contingency_table)
      
       NEGATIVE POSITIVE
  I/II      162       15
  III       172       56
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 16.741, df = 1, p-value = 4.285e-05

#Demographics Table

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    pT,
    pN,
    Stage,
    Grade,
    NAC,
    ACT,
    MSI,
    BRAF.V600E,
    RAS,
    DFS.Event,
    OS.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon")),
    pT = factor(pT, levels = c("T0","T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    Stage = factor(Stage, levels = c("I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3","GX")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF WT", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS WT", "RAS Mut")),
    DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
Characteristic N = 7951
Age 61 (13 - 91)
Gender
    Male 407 (51%)
    Female 388 (49%)
PrimSite
    Right-sided colon 411 (52%)
    Left-sided colon 384 (48%)
pT
    T0 3 (0.4%)
    T1-T2 133 (17%)
    T3-T4 654 (83%)
    Unknown 5
pN
    N0 310 (39%)
    N1-N2 482 (61%)
    Unknown 3
Stage
    I 47 (5.9%)
    II 262 (33%)
    III 486 (61%)
Grade
    G1 84 (11%)
    G2 559 (72%)
    G3 127 (16%)
    GX 4 (0.5%)
    Unknown 21
NAC
    Neoadjuvant Chemotherapy 0 (0%)
    Upfront Surgery 795 (100%)
ACT
    Adjuvant Chemotherapy 522 (66%)
    Observation 273 (34%)
MSI
    MSS 664 (84%)
    MSI-High 131 (16%)
BRAF.V600E
    BRAF WT 699 (88%)
    BRAF V600E 96 (12%)
RAS
    RAS WT 459 (58%)
    RAS Mut 336 (42%)
DFS.Event
    Recurrence 141 (18%)
    No Recurrence 654 (82%)
OS.months 27 (0 - 103)
1 Median (Range); n (%)
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE,
  strip_md_bold = TRUE)
Warning: The `strip_md_bold` argument of `as_flex_table()` is deprecated as of gtsummary 1.6.0.
fit1

Characteristic

N = 7951

Age

61 (13 - 91)

Gender

Male

407 (51%)

Female

388 (49%)

PrimSite

Right-sided colon

411 (52%)

Left-sided colon

384 (48%)

pT

T0

3 (0.4%)

T1-T2

133 (17%)

T3-T4

654 (83%)

Unknown

5

pN

N0

310 (39%)

N1-N2

482 (61%)

Unknown

3

Stage

I

47 (5.9%)

II

262 (33%)

III

486 (61%)

Grade

G1

84 (11%)

G2

559 (72%)

G3

127 (16%)

GX

4 (0.5%)

Unknown

21

NAC

Neoadjuvant Chemotherapy

0 (0%)

Upfront Surgery

795 (100%)

ACT

Adjuvant Chemotherapy

522 (66%)

Observation

273 (34%)

MSI

MSS

664 (84%)

MSI-High

131 (16%)

BRAF.V600E

BRAF WT

699 (88%)

BRAF V600E

96 (12%)

RAS

RAS WT

459 (58%)

RAS Mut

336 (42%)

DFS.Event

Recurrence

141 (18%)

No Recurrence

654 (82%)

OS.months

27 (0 - 103)

1Median (Range); n (%)

save_as_docx(fit1, path= "~/Downloads/table1.docx")

#Heatmap with Clinical & Genomics Factors

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)

ha <- HeatmapAnnotation(
  Stage = circ_data$Stage,
  Gender = circ_data$Gender,
  PrimSite = circ_data$PrimSite,
  pT = circ_data$pT,
  pN = circ_data$pN,
  Grade = circ_data$Grade,
  ACT = circ_data$ACT,
  MSI = circ_data$MSI,
  BRAF.V600E = circ_data$BRAF.V600E,
  RAS = circ_data$RAS,
  ctDNA.MRD = circ_data$ctDNA.MRD,
  ctDNA.Surveillance = circ_data$ctDNA.Surveillance,
  ctDNA.anytime = circ_data$ctDNA.anytime,
  DFS.Event = circ_data$DFS.Event,
  
  col = list(Stage = c("I" = "seagreen1", "II" = "orange", "III" = "purple"),
    Gender = c("Female" = "goldenrod" , "Male" = "blue4"),
    PrimSite = c("Right-sided colon" = "brown", "Left-sided colon" ="darkgreen"),
    pT = c("T0" = "khaki","T1-T2" = "khaki", "T3-T4" ="brown2"),
    pN = c("N0" = "cornflowerblue", "N1-N2" ="orange2"),
    Grade = c("GX" = "grey","G1" = "coral", "G2" ="darkgreen", "G3" = "yellow3"),
    ACT = c("TRUE" = "#C1211A", "FALSE" ="#008BCE"),
    MSI = c("MSS" = "grey", "MSI-High" ="black"),
    BRAF.V600E = c("WT" = "grey", "MUT" ="black"),
    RAS = c("WT" = "grey", "MUT" ="black"),
    ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.Surveillance = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.anytime = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    DFS.Event = c("TRUE" = "red3", "FALSE" ="blue")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()

#DFS by ctDNA at the MRD Window - all stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                     n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 328     29     NA      NA      NA
ctDNA.MRD=POSITIVE  62     36   11.3    8.67      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000      110.000       25.000        0.903        0.019        0.859        0.935 

                ctDNA.MRD=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      12.0000      36.0000       0.3608       0.0669       0.2332       0.4898 
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 390, number of events= 65 

                    coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.MRDPOSITIVE 2.2879    9.8539   0.2517 9.088   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     9.854     0.1015     6.016     16.14

Concordance= 0.742  (se = 0.029 )
Likelihood ratio test= 74.36  on 1 df,   p=<2e-16
Wald test            = 82.6  on 1 df,   p=<2e-16
Score (logrank) test = 124  on 1 df,   p=<2e-16
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 9.85 (6.02-16.14); p = 0"

#ctDNA sample positive in the MRD Window - 2-10 week intervals

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$MRD.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
filtered_data <- circ_data %>%
  filter(ctDNA.MRD >= 2 & ctDNA.MRD <= 10) #intervals for 2-10 weeks
filtered_data$ctDNA_bucket <- cut(filtered_data$ctDNA.MRD, 
                                  breaks = c(2, 4, 6, 8, 10), 
                                  right = FALSE, 
                                  labels = c("2-4", "4-6", "6-8", "8-10"))
filtered_data <- filtered_data %>%
  filter(!is.na(ctDNA_bucket))
rate_by_bucket <- filtered_data %>%
  group_by(ctDNA_bucket) %>%
  summarise(
    n_total = n(),  # Total number of patients in the bucket
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
overall_stats <- filtered_data %>%
  summarise(
    total_samples = n(),
    total_positive = sum(biomarker_status == "POSITIVE"),
    total_negative = sum(biomarker_status == "NEGATIVE"),
    overall_percentage_positive = mean(biomarker_status == "POSITIVE") * 100
  )

combined_results <- bind_rows(rate_by_bucket, overall_stats)
print(combined_results)

# Create the stacked bar plot for positivity and negativity rates by bucket
bar_midpoints <- barplot(
  t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])),  # Transpose to get the correct format
  names.arg = rate_by_bucket$ctDNA_bucket,
  col = c("red", "blue"),  # Colors: red for positive, blue for negative
  main = '% ctDNA Positive and Negative Samples at the MRD Window',
  xlab = 'Weeks from Surgery',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)


rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$MRD.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
filtered_data <- circ_data %>%
  filter(ctDNA.MRD >= 0 & ctDNA.MRD <= 10) #intervals for 0-10 weeks
filtered_data$ctDNA_bucket <- cut(filtered_data$ctDNA.MRD, 
                                  breaks = c(0, 2, 4, 6, 8, 10), 
                                  right = FALSE, 
                                  labels = c("0-2","2-4", "4-6", "6-8", "8-10"))
filtered_data <- filtered_data %>%
  filter(!is.na(ctDNA_bucket))
rate_by_bucket <- filtered_data %>%
  group_by(ctDNA_bucket) %>%
  summarise(
    n_total = n(),  # Total number of patients in the bucket
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
overall_stats <- filtered_data %>%
  summarise(
    total_samples = n(),
    total_positive = sum(biomarker_status == "POSITIVE"),
    total_negative = sum(biomarker_status == "NEGATIVE"),
    overall_percentage_positive = mean(biomarker_status == "POSITIVE") * 100
  )

combined_results <- bind_rows(rate_by_bucket, overall_stats)
print(combined_results)

# Create the stacked bar plot for positivity and negativity rates by bucket
bar_midpoints <- barplot(
  t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])),  # Transpose to get the correct format
  names.arg = rate_by_bucket$ctDNA_bucket,
  col = c("red", "blue"),  # Colors: red for positive, blue for negative
  main = '% ctDNA Positive and Negative Samples at the MRD Window',
  xlab = 'Weeks from Surgery',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)

#Median number of timepoints in the MRD Window

rm(list=ls())
setwd("~/Downloads")
filtered_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
filtered_data <- filtered_data[filtered_data$MRD.Window==TRUE,]
filtered_data <- filtered_data[!is.na(filtered_data$cfDNAconc), ]
filtered_data <- filtered_data %>%
  filter(ctDNA.MRD >= 2 & ctDNA.MRD <= 10) #intervals for 2-10 weeks

# Calculate the median number of ctDNA tests per patient
median_ctDNA_tests <- filtered_data %>%
  group_by(pts_id) %>%
  summarise(num_tests = n()) %>%
  summarise(median_tests = median(num_tests))
ctDNA_stats <- filtered_data %>%
  group_by(pts_id) %>%
  tally() %>%
  summarise(
    median_tests = median(n),
    min_tests = min(n),
    max_tests = max(n)
  )

print(median_ctDNA_tests)
print(ctDNA_stats)

#Multivariate cox regression for DFS at the MRD Window & Age threshold as 50 years - all stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group2 + PrimSite + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS", refLabel = "Reference Group")

test.ph <- cox.zph(cox_fit)

# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_fit)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)

#Univariate cox regression for factors used in MVA - all stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male")) #univariate for gender
cox_fit <- coxph(surv_object ~ Gender, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ Gender, data = circ_data)

  n= 390, number of events= 65 

             coef exp(coef) se(coef)     z Pr(>|z|)
GenderMale 0.3259    1.3853   0.2550 1.278    0.201

           exp(coef) exp(-coef) lower .95 upper .95
GenderMale     1.385     0.7219    0.8404     2.284

Concordance= 0.535  (se = 0.032 )
Likelihood ratio test= 1.67  on 1 df,   p=0.2
Wald test            = 1.63  on 1 df,   p=0.2
Score (logrank) test = 1.65  on 1 df,   p=0.2
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.39 (0.84-2.28); p = 0.201"
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50")) #univariate for Age
cox_fit <- coxph(surv_object ~ Age.Group2, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ Age.Group2, data = circ_data)

  n= 390, number of events= 65 

                coef exp(coef) se(coef)     z Pr(>|z|)
Age.Group2≥50 0.1934    1.2133   0.3102 0.623    0.533

              exp(coef) exp(-coef) lower .95 upper .95
Age.Group2≥50     1.213     0.8242    0.6606     2.229

Concordance= 0.526  (se = 0.024 )
Likelihood ratio test= 0.4  on 1 df,   p=0.5
Wald test            = 0.39  on 1 df,   p=0.5
Score (logrank) test = 0.39  on 1 df,   p=0.5
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.21 (0.66-2.23); p = 0.533"
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon")) #univariate for Tumor Location
cox_fit <- coxph(surv_object ~ PrimSite, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ PrimSite, data = circ_data)

  n= 390, number of events= 65 

                           coef exp(coef) se(coef)     z Pr(>|z|)
PrimSiteLeft-sided colon 0.2250    1.2523   0.2482 0.907    0.365

                         exp(coef) exp(-coef) lower .95 upper .95
PrimSiteLeft-sided colon     1.252     0.7985      0.77     2.037

Concordance= 0.518  (se = 0.032 )
Likelihood ratio test= 0.82  on 1 df,   p=0.4
Wald test            = 0.82  on 1 df,   p=0.4
Score (logrank) test = 0.83  on 1 df,   p=0.4
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.25 (0.77-2.04); p = 0.365"
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4")) #univariate for Overall T Stage
cox_fit <- coxph(surv_object ~ pT, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ pT, data = circ_data)

  n= 386, number of events= 65 
   (4 observations deleted due to missingness)

          coef exp(coef) se(coef)     z Pr(>|z|)  
pTT3-T4 1.0960    2.9923   0.5163 2.123   0.0338 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

        exp(coef) exp(-coef) lower .95 upper .95
pTT3-T4     2.992     0.3342     1.088     8.231

Concordance= 0.549  (se = 0.017 )
Likelihood ratio test= 6.26  on 1 df,   p=0.01
Wald test            = 4.51  on 1 df,   p=0.03
Score (logrank) test = 4.98  on 1 df,   p=0.03
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 2.99 (1.09-8.23); p = 0.034"
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2")) #univariate for Overall N Stage
cox_fit <- coxph(surv_object ~ pN, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ pN, data = circ_data)

  n= 388, number of events= 65 
   (2 observations deleted due to missingness)

          coef exp(coef) se(coef)     z Pr(>|z|)    
pNN1-N2 1.6966    5.4556   0.3592 4.723 2.32e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

        exp(coef) exp(-coef) lower .95 upper .95
pNN1-N2     5.456     0.1833     2.698     11.03

Concordance= 0.667  (se = 0.023 )
Likelihood ratio test= 31.89  on 1 df,   p=2e-08
Wald test            = 22.31  on 1 df,   p=2e-06
Score (logrank) test = 28.18  on 1 df,   p=1e-07
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 5.46 (2.7-11.03); p = 0"
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High")) #univariate for MSI
cox_fit <- coxph(surv_object ~ MSI, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ MSI, data = circ_data)

  n= 390, number of events= 65 

              coef exp(coef) se(coef)     z Pr(>|z|)
MSIMSI-High 0.2018    1.2236   0.3312 0.609    0.542

            exp(coef) exp(-coef) lower .95 upper .95
MSIMSI-High     1.224     0.8173    0.6393     2.342

Concordance= 0.531  (se = 0.028 )
Likelihood ratio test= 0.35  on 1 df,   p=0.6
Wald test            = 0.37  on 1 df,   p=0.5
Score (logrank) test = 0.37  on 1 df,   p=0.5
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.22 (0.64-2.34); p = 0.542"
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E")) #univariate for BRAF
cox_fit <- coxph(surv_object ~ BRAF.V600E, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ BRAF.V600E, data = circ_data)

  n= 390, number of events= 65 

                  coef exp(coef) se(coef)     z Pr(>|z|)  
BRAF.V600EV600E 0.5464    1.7271   0.3199 1.708   0.0876 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
BRAF.V600EV600E     1.727      0.579    0.9226     3.233

Concordance= 0.552  (se = 0.028 )
Likelihood ratio test= 2.59  on 1 df,   p=0.1
Wald test            = 2.92  on 1 df,   p=0.09
Score (logrank) test = 2.99  on 1 df,   p=0.08
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.73 (0.92-3.23); p = 0.088"
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut")) #univariate for RAS
cox_fit <- coxph(surv_object ~ RAS, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ RAS, data = circ_data)

  n= 390, number of events= 65 

         coef exp(coef) se(coef)     z Pr(>|z|)
RASMut 0.1691    1.1843   0.2507 0.675      0.5

       exp(coef) exp(-coef) lower .95 upper .95
RASMut     1.184     0.8444    0.7246     1.936

Concordance= 0.511  (se = 0.031 )
Likelihood ratio test= 0.45  on 1 df,   p=0.5
Wald test            = 0.46  on 1 df,   p=0.5
Score (logrank) test = 0.46  on 1 df,   p=0.5
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.18 (0.72-1.94); p = 0.5"

#DFS by ctDNA at the MRD Window - Stage II

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                     n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 141      5     NA      NA      NA
ctDNA.MRD=POSITIVE  10      3     NA    11.1      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       46.000        5.000        0.949        0.023        0.878        0.979 

                ctDNA.MRD=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        6.000        3.000        0.700        0.145        0.329        0.892 
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 151, number of events= 8 

                    coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.MRDPOSITIVE 2.1194    8.3262   0.7317 2.897  0.00377 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     8.326     0.1201     1.985     34.93

Concordance= 0.68  (se = 0.09 )
Likelihood ratio test= 6.3  on 1 df,   p=0.01
Wald test            = 8.39  on 1 df,   p=0.004
Score (logrank) test = 12.02  on 1 df,   p=5e-04
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 8.33 (1.98-34.93); p = 0.004"

#DFS by ctDNA at the MRD Window - Stage III

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                     n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 170     24     NA      NA      NA
ctDNA.MRD=POSITIVE  51     33     10    7.13    16.2
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      57.0000      20.0000       0.8582       0.0303       0.7863       0.9073 

                ctDNA.MRD=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       6.0000      33.0000       0.2704       0.0714       0.1433       0.4146 
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 221, number of events= 57 

                    coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.MRDPOSITIVE 2.0479    7.7515   0.2728 7.506 6.08e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     7.752      0.129     4.541     13.23

Concordance= 0.731  (se = 0.031 )
Likelihood ratio test= 53.81  on 1 df,   p=2e-13
Wald test            = 56.35  on 1 df,   p=6e-14
Score (logrank) test = 77.35  on 1 df,   p=<2e-16
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 7.75 (4.54-13.23); p = 0"

#DFS by ctDNA at the MRD Window - Stage II & Risk Groups

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$Risk.Group!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.Risk = case_when(
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[!is.na(circ_data$ctDNA.Stage.II.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.Risk, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Stage.II.Risk, data = circ_data)

                        n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.Risk=1  30      1     NA      NA      NA
ctDNA.Stage.II.Risk=2   2      0     NA      NA      NA
ctDNA.Stage.II.Risk=3 110      4     NA      NA      NA
ctDNA.Stage.II.Risk=4   8      3     NA    11.1      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Stage.II.Risk) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.Risk, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage II Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.Stage.II.Risk, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Stage.II.Risk=1 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      11.0000       1.0000       0.9600       0.0392       0.7484       0.9943 

                ctDNA.Stage.II.Risk=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
          24            1            0            1            0           NA           NA 

                ctDNA.Stage.II.Risk=3 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      35.0000       4.0000       0.9465       0.0278       0.8549       0.9809 

                ctDNA.Stage.II.Risk=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        5.000        3.000        0.625        0.171        0.229        0.861 
circ_data$ctDNA.Stage.II.Risk <- factor(circ_data$ctDNA.Stage.II.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxphf(surv_object ~ ctDNA.Stage.II.Risk, data=circ_data) 
summary(cox_fit)
coxphf(formula = surv_object ~ ctDNA.Stage.II.Risk, data = circ_data)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood 

                                               coef se(coef)  exp(coef) lower 0.95 upper 0.95       Chisq          p
ctDNA.Stage.II.RiskctDNA(+) & Low Risk   1.54746514 1.827452  4.6995424 0.03219659   88.15970 0.670679229 0.41281490
ctDNA.Stage.II.RiskctDNA(-) & High Risk -0.06180044 1.054866  0.9400705 0.17388788    9.38239 0.004245486 0.94804868
ctDNA.Stage.II.RiskctDNA(+) & High Risk  2.30998495 1.092244 10.0742730 1.65326183  104.09858 6.196660240 0.01279916

Likelihood ratio test=9.240477 on 3 df, p=0.02625872, n=150
Wald test = 9.971067 on 3 df, p = 0.01881368

Covariance-Matrix:
                                        ctDNA.Stage.II.RiskctDNA(+) & Low Risk ctDNA.Stage.II.RiskctDNA(-) & High Risk ctDNA.Stage.II.RiskctDNA(+) & High Risk
ctDNA.Stage.II.RiskctDNA(+) & Low Risk                               3.3395808                               0.8333478                               0.8321149
ctDNA.Stage.II.RiskctDNA(-) & High Risk                              0.8333478                               1.1127416                               0.8335422
ctDNA.Stage.II.RiskctDNA(+) & High Risk                              0.8321149                               0.8335422                               1.1929966

#DFS by ctDNA at the MRD Window - Stage III & Risk Groups

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.III.Risk = case_when(
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[!is.na(circ_data$ctDNA.Stage.III.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Stage.III.Risk, data = circ_data)

                         n events median 0.95LCL 0.95UCL
ctDNA.Stage.III.Risk=1 103     10     NA      NA      NA
ctDNA.Stage.III.Risk=2  13      9   12.9    6.14      NA
ctDNA.Stage.III.Risk=3  67     14     NA      NA      NA
ctDNA.Stage.III.Risk=4  38     24    9.0    6.01      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Stage.III.Risk) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")

summary(KM_curve, times= c(18, 24))
Call: survfit(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Stage.III.Risk=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   18     57       8    0.909  0.0312        0.825        0.954
   24     34       1    0.889  0.0367        0.791        0.942

                ctDNA.Stage.III.Risk=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     18.0000       2.0000       8.0000       0.3077       0.1417       0.0793       0.5780 

                ctDNA.Stage.III.Risk=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   18     38      11    0.814  0.0512        0.688        0.893
   24     23       0    0.814  0.0512        0.688        0.893

                ctDNA.Stage.III.Risk=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   18      8      24    0.304  0.0812        0.157        0.464
   24      6       0    0.304  0.0812        0.157        0.464
circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data)

  n= 221, number of events= 57 

                                            coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.Stage.III.RiskctDNA(+) & Low Risk   2.4892   12.0518   0.4642 5.362 8.23e-08 ***
ctDNA.Stage.III.RiskctDNA(-) & High Risk  0.7524    2.1221   0.4141 1.817   0.0692 .  
ctDNA.Stage.III.RiskctDNA(+) & High Risk  2.3908   10.9222   0.3791 6.307 2.85e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                         exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.III.RiskctDNA(+) & Low Risk     12.052    0.08297    4.8517    29.937
ctDNA.Stage.III.RiskctDNA(-) & High Risk     2.122    0.47123    0.9425     4.778
ctDNA.Stage.III.RiskctDNA(+) & High Risk    10.922    0.09156    5.1955    22.961

Concordance= 0.757  (se = 0.033 )
Likelihood ratio test= 57.24  on 3 df,   p=2e-12
Wald test            = 55.9  on 3 df,   p=4e-12
Score (logrank) test = 79.25  on 3 df,   p=<2e-16
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.III.Risk = case_when(
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Stage.III.Risk, data = circ_data)

                         n events median 0.95LCL 0.95UCL
ctDNA.Stage.III.Risk=1 103     10     NA      NA      NA
ctDNA.Stage.III.Risk=2  13      9   12.9    6.14      NA
ctDNA.Stage.III.Risk=3  67     14     NA      NA      NA
ctDNA.Stage.III.Risk=4  38     24    9.0    6.01      NA
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")

summary(KM_curve, times= c(18))
Call: survfit(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Stage.III.Risk=1 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     18.0000      57.0000       8.0000       0.9095       0.0312       0.8248       0.9543 

                ctDNA.Stage.III.Risk=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     18.0000       2.0000       8.0000       0.3077       0.1417       0.0793       0.5780 

                ctDNA.Stage.III.Risk=3 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     18.0000      38.0000      11.0000       0.8141       0.0512       0.6878       0.8932 

                ctDNA.Stage.III.Risk=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     18.0000       8.0000      24.0000       0.3038       0.0812       0.1574       0.4641 
circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data)

  n= 221, number of events= 57 

                          coef exp(coef) se(coef)      z Pr(>|z|)    
ctDNA.Stage.III.Risk4 -0.09842   0.90627  0.39258 -0.251    0.802    
ctDNA.Stage.III.Risk1 -2.48922   0.08297  0.46424 -5.362 8.23e-08 ***
ctDNA.Stage.III.Risk3 -1.73680   0.17608  0.43279 -4.013 5.99e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                      exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.III.Risk4   0.90627      1.103   0.41985    1.9562
ctDNA.Stage.III.Risk1   0.08297     12.052   0.03340    0.2061
ctDNA.Stage.III.Risk3   0.17608      5.679   0.07539    0.4113

Concordance= 0.757  (se = 0.033 )
Likelihood ratio test= 57.24  on 3 df,   p=2e-12
Wald test            = 55.9  on 3 df,   p=4e-12
Score (logrank) test = 79.25  on 3 df,   p=<2e-16

#OS by ctDNA at the MRD Window - all stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                     n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 334      8     NA      NA      NA
ctDNA.MRD=POSITIVE  71      7   40.4    40.4      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
    2.40e+01     1.52e+02     7.00e+00     9.75e-01     9.49e-03     9.48e-01     9.88e-01 

                ctDNA.MRD=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      30.0000       5.0000       0.9064       0.0408       0.7856       0.9607 
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 405, number of events= 15 

                   coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.MRDPOSITIVE 1.551     4.717    0.519 2.988   0.0028 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     4.717      0.212     1.705     13.04

Concordance= 0.633  (se = 0.068 )
Likelihood ratio test= 7.93  on 1 df,   p=0.005
Wald test            = 8.93  on 1 df,   p=0.003
Score (logrank) test = 10.85  on 1 df,   p=0.001
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 4.72 (1.71-13.04); p = 0.003"

#DFS by ctDNA at the Surveillance Window - all stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                              n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 540     20     NA      NA      NA
ctDNA.Surveillance=POSITIVE  83     54   16.2    13.7    28.4
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
    2.40e+01     2.83e+02     1.60e+01     9.64e-01     9.06e-03     9.41e-01     9.78e-01 

                ctDNA.Surveillance=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      21.0000      44.0000       0.4087       0.0597       0.2916       0.5222 
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

  n= 623, number of events= 74 

                              coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.SurveillancePOSITIVE  3.2926   26.9135   0.2645 12.45   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE     26.91    0.03716     16.03     45.19

Concordance= 0.817  (se = 0.026 )
Likelihood ratio test= 173.2  on 1 df,   p=<2e-16
Wald test            = 155  on 1 df,   p=<2e-16
Score (logrank) test = 344  on 1 df,   p=<2e-16
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 26.91 (16.03-45.19); p = 0"

#DFS by ctDNA at the Surveillance Window - Stages II

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                              n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 198      6     NA      NA      NA
ctDNA.Surveillance=POSITIVE  16      9   21.2    11.1      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       99.000        5.000        0.967        0.015        0.920        0.986 

                ctDNA.Surveillance=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        3.000        7.000        0.480        0.149        0.187        0.725 
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

  n= 214, number of events= 15 

                              coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.SurveillancePOSITIVE  3.4093   30.2449   0.5373 6.345 2.23e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE     30.24    0.03306     10.55      86.7

Concordance= 0.782  (se = 0.063 )
Likelihood ratio test= 34.66  on 1 df,   p=4e-09
Wald test            = 40.26  on 1 df,   p=2e-10
Score (logrank) test = 94.31  on 1 df,   p=<2e-16
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 30.24 (10.55-86.7); p = 0"

#DFS by ctDNA at the Surveillance Window - Stages III

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                              n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 306     13     NA      NA      NA
ctDNA.Surveillance=POSITIVE  64     45   15.8    13.1    28.4
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000     163.0000      10.0000       0.9610       0.0123       0.9281       0.9790 

                ctDNA.Surveillance=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      18.0000      37.0000       0.3800       0.0646       0.2553       0.5037 
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

  n= 370, number of events= 58 

                              coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.SurveillancePOSITIVE  3.2208   25.0476   0.3173 10.15   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE     25.05    0.03992     13.45     46.65

Concordance= 0.827  (se = 0.027 )
Likelihood ratio test= 129.4  on 1 df,   p=<2e-16
Wald test            = 103  on 1 df,   p=<2e-16
Score (logrank) test = 222.1  on 1 df,   p=<2e-16
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 25.05 (13.45-46.65); p = 0"

#OS by ctDNA at the Surveillance Window - all stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                              n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 540      4     NA      NA      NA
ctDNA.Surveillance=POSITIVE  83      4     NA      NA      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
    2.40e+01     3.32e+02     3.00e+00     9.94e-01     3.65e-03     9.80e-01     9.98e-01 

                ctDNA.Surveillance=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      41.0000       3.0000       0.9594       0.0231       0.8785       0.9868 
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

  n= 623, number of events= 8 

                             coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.SurveillancePOSITIVE 1.9570    7.0779   0.7088 2.761  0.00576 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE     7.078     0.1413     1.764     28.39

Concordance= 0.687  (se = 0.094 )
Likelihood ratio test= 6.66  on 1 df,   p=0.01
Wald test            = 7.62  on 1 df,   p=0.006
Score (logrank) test = 10.36  on 1 df,   p=0.001
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 7.08 (1.76-28.39); p = 0.006"

#ctDNA sample positive in the Surveillance Window - 6mo intervals

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$Surveillance.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
circ_data$ctDNA_bucket <- cut(circ_data$ctDNA.Surv, 
                              breaks = seq(0, max(circ_data$ctDNA.Surv, na.rm = TRUE), by = 6), 
                              right = FALSE, 
                              labels = paste0(seq(0, max(circ_data$ctDNA.Surv, na.rm = TRUE) - 6, by = 6), 
                                              "-", 
                                              seq(6, max(circ_data$ctDNA.Surv, na.rm = TRUE), by = 6)))

rate_by_bucket <- circ_data %>%
  group_by(ctDNA_bucket) %>%
  summarise(
    n_total = n(),  # Total number of patients in the bucket
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
overall_totals <- circ_data %>%
  summarise(
    ctDNA_bucket = "Total",  # Label for the total row
    n_total = n(),  # Total number of patients across all buckets
    n_positive = sum(biomarker_status == "POSITIVE"),  # Total number of positive cases across all buckets
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Overall positivity rate
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Total number of negative cases across all buckets
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Overall negativity rate
  )
positivity_rate_with_total <- bind_rows(rate_by_bucket, overall_totals)
print(positivity_rate_with_total)
bar_midpoints <- barplot(
  t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])),  # Transpose to get correct format
  names.arg = rate_by_bucket$ctDNA_bucket,
  col = c("red", "blue"),  # Red for positivity and blue for negativity
  main = '% ctDNA Positive and Negative Samples by 6-month Interval',
  xlab = 'Time from Start of Surveillance (Months)',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)

#Median number of timepoints in the Surveillance Window

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$Surveillance.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]

# Calculate the median number of ctDNA tests per patient
ctDNA_stats <- circ_data %>%
  group_by(pts_id) %>%
  tally() %>%
  summarise(
    median_tests = median(n),
    min_tests = min(n),
    max_tests = max(n)
  )

# Print the result
print(ctDNA_stats)

#Time between imaging at the Surveillance window by ctDNA status

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC Imaging Frequency.csv")
tally(~ctDNA_Surveillance, data=circ_data, margins = TRUE)
ctDNA_Surveillance
NEGATIVE POSITIVE    Total 
     151       22      173 
median_AverageDateDifference <- aggregate(AverageDateDifference ~ ctDNA_Surveillance, data = circ_data, FUN = median)
print(median_AverageDateDifference)
circ_data$ctDNA_Surveillance <- factor(circ_data$ctDNA_Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative (n=151)","Positive (n=22)"))
boxplot(AverageDateDifference~ctDNA_Surveillance, data=circ_data, main="Average Time between Imaging at Surveillance Window", xlab="ctDNA at the Surveillance Window", ylab="Average Time Between Imaging (Days)", col="white",border="black")

m1<-wilcox.test(AverageDateDifference ~ ctDNA_Surveillance, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

    Wilcoxon rank sum test with continuity correction

data:  AverageDateDifference by ctDNA_Surveillance
W = 2327, p-value = 6.322e-06
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 106.25 269.00
sample estimates:
difference in location 
                   181 
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC Imaging Frequency.csv")
tally(~ctDNA_Surveillance, data=circ_data, margins = TRUE)
ctDNA_Surveillance
NEGATIVE POSITIVE    Total 
     151       22      173 
median_MedianDateDifference <- aggregate(MedianDateDifference ~ ctDNA_Surveillance, data = circ_data, FUN = median)
print(median_MedianDateDifference)
circ_data$ctDNA_Surveillance <- factor(circ_data$ctDNA_Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative (n=151)","Positive (n=22)"))
boxplot(MedianDateDifference~ctDNA_Surveillance, data=circ_data, main="Median Time between Imaging at Surveillance Window", xlab="ctDNA at the Surveillance Window", ylab="Median Time Between Imaging (Days)", col="white",border="black")

m2<-wilcox.test(MedianDateDifference ~ ctDNA_Surveillance, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)

    Wilcoxon rank sum test with continuity correction

data:  MedianDateDifference by ctDNA_Surveillance
W = 2336.5, p-value = 5.008e-06
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
  90.00009 269.00006
sample estimates:
difference in location 
              158.7506 

#Time-dependent analysis in surveillance window - all stages

rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
Warning: Stop time must be > start time, NA created
summary(fit)
Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status, 
    data = dt_final)

  n= 3456, number of events= 95 
   (1211 observations deleted due to missingness)

                            coef exp(coef) se(coef)     z Pr(>|z|)    
biomarker_statusPOSITIVE  3.6369   37.9742   0.2235 16.27   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                         exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE     37.97    0.02633     24.51     58.84

Concordance= 0.816  (se = 0.024 )
Likelihood ratio test= 256.4  on 1 df,   p=<2e-16
Wald test            = 264.9  on 1 df,   p=<2e-16
Score (logrank) test = 679.7  on 1 df,   p=<2e-16

#Time-dependent analysis in surveillance window - all stages ACT-treated

rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
Warning: Stop time must be > start time, NA created
summary(fit)
Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status, 
    data = dt_final)

  n= 2178, number of events= 69 
   (1014 observations deleted due to missingness)

                            coef exp(coef) se(coef)     z Pr(>|z|)    
biomarker_statusPOSITIVE  3.5174   33.6971   0.2601 13.52   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                         exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE      33.7    0.02968     20.24     56.11

Concordance= 0.807  (se = 0.029 )
Likelihood ratio test= 175.9  on 1 df,   p=<2e-16
Wald test            = 182.8  on 1 df,   p=<2e-16
Score (logrank) test = 443.9  on 1 df,   p=<2e-16

#Time-dependent analysis in surveillance window - all stages Non-ACT-treated

rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="FALSE",]
dt_final <- dt_final[dt_final$tstart!="",]

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
Warning: Stop time must be > start time, NA created
summary(fit)
Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status, 
    data = dt_final)

  n= 1278, number of events= 26 
   (197 observations deleted due to missingness)

                            coef exp(coef) se(coef)    z Pr(>|z|)    
biomarker_statusPOSITIVE  3.8078   45.0513   0.4362 8.73   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                         exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE     45.05     0.0222     19.16     105.9

Concordance= 0.835  (se = 0.043 )
Likelihood ratio test= 76.31  on 1 df,   p=<2e-16
Wald test            = 76.21  on 1 df,   p=<2e-16
Score (logrank) test = 214.3  on 1 df,   p=<2e-16

#DFS by ctDNA subgroups at the Surveillance window

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Surveillance.Groups!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~Surveillance.Groups, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    Surveillance.Groups, data = circ_data)

                                        n events median 0.95LCL 0.95UCL
Surveillance.Groups=All Negative      540     20     NA      NA      NA
Surveillance.Groups=All Positive       35     30   8.05     6.9    13.6
Surveillance.Groups=Negative-Positive  48     24  31.04    21.3      NA
event_summary <- circ_data %>%
  group_by(Surveillance.Groups) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Surveillance.Groups <- factor(circ_data$Surveillance.Groups, levels=c("All Negative","Negative-Positive","All Positive"))
KM_curve <- survfit(surv_object ~ Surveillance.Groups, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","purple", "red"), title="DFS - ctDNA Subgroups Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("All Negative","Negative-Positive","All Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ Surveillance.Groups, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                Surveillance.Groups=All Negative 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
    2.40e+01     2.83e+02     1.60e+01     9.64e-01     9.06e-03     9.41e-01     9.78e-01 

                Surveillance.Groups=Negative-Positive 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      17.0000      17.0000       0.5803       0.0801       0.4086       0.7182 

                Surveillance.Groups=All Positive 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       4.0000      27.0000       0.1657       0.0702       0.0576       0.3222 
cox_fit <- coxph(surv_object ~ Surveillance.Groups, data=circ_data)
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ Surveillance.Groups, data = circ_data)

  n= 623, number of events= 74 

                                        coef exp(coef) se(coef)      z Pr(>|z|)    
Surveillance.GroupsNegative-Positive  2.7806   16.1288   0.3041  9.144   <2e-16 ***
Surveillance.GroupsAll Positive       4.2247   68.3543   0.3033 13.929   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                     exp(coef) exp(-coef) lower .95 upper .95
Surveillance.GroupsNegative-Positive     16.13    0.06200     8.887     29.27
Surveillance.GroupsAll Positive          68.35    0.01463    37.721    123.86

Concordance= 0.833  (se = 0.027 )
Likelihood ratio test= 198.8  on 2 df,   p=<2e-16
Wald test            = 196.3  on 2 df,   p=<2e-16
Score (logrank) test = 536.6  on 2 df,   p=<2e-16
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Surveillance.Groups!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Surveillance.Groups <- factor(circ_data$Surveillance.Groups, levels=c("Negative-Positive","All Positive", "All Negative"))
cox_fit <- coxph(surv_object ~ Surveillance.Groups, data=circ_data)
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ Surveillance.Groups, data = circ_data)

  n= 623, number of events= 74 

                                   coef exp(coef) se(coef)      z Pr(>|z|)    
Surveillance.GroupsAll Positive  1.4441    4.2380   0.2826  5.111  3.2e-07 ***
Surveillance.GroupsAll Negative -2.7806    0.0620   0.3041 -9.144  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                exp(coef) exp(-coef) lower .95 upper .95
Surveillance.GroupsAll Positive     4.238      0.236   2.43591    7.3734
Surveillance.GroupsAll Negative     0.062     16.129   0.03416    0.1125

Concordance= 0.833  (se = 0.027 )
Likelihood ratio test= 198.8  on 2 df,   p=<2e-16
Wald test            = 196.3  on 2 df,   p=<2e-16
Score (logrank) test = 536.6  on 2 df,   p=<2e-16

#Demographics table for ctDNA positive at the Surveillance window with no radiological recurrence

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance=="POSITIVE",]
circ_data <- circ_data[circ_data$DFS.Event==FALSE,]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    pT,
    pN,
    Stage,
    Grade,
    NAC,
    ACT,
    MSI,
    BRAF.V600E,
    RAS,
    OS.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon")),
    pT = factor(pT, levels = c("T0","T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    Stage = factor(Stage, levels = c("I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3","GX")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF WT", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS WT", "RAS Mut")),
    OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
Characteristic N = 291
Age 57 (28 - 86)
Gender
    Male 12 (41%)
    Female 17 (59%)
PrimSite
    Right-sided colon 12 (41%)
    Left-sided colon 17 (59%)
pT
    T0 0 (0%)
    T1-T2 4 (14%)
    T3-T4 25 (86%)
pN
    N0 9 (31%)
    N1-N2 20 (69%)
Stage
    I 3 (10%)
    II 7 (24%)
    III 19 (66%)
Grade
    G1 4 (14%)
    G2 19 (66%)
    G3 6 (21%)
    GX 0 (0%)
NAC
    Neoadjuvant Chemotherapy 0 (0%)
    Upfront Surgery 29 (100%)
ACT
    Adjuvant Chemotherapy 19 (66%)
    Observation 10 (34%)
MSI
    MSS 26 (90%)
    MSI-High 3 (10%)
BRAF.V600E
    BRAF WT 26 (90%)
    BRAF V600E 3 (10%)
RAS
    RAS WT 17 (59%)
    RAS Mut 12 (41%)
OS.months 22 (7 - 62)
1 Median (Range); n (%)
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE,
  strip_md_bold = TRUE)
fit1

Characteristic

N = 291

Age

57 (28 - 86)

Gender

Male

12 (41%)

Female

17 (59%)

PrimSite

Right-sided colon

12 (41%)

Left-sided colon

17 (59%)

pT

T0

0 (0%)

T1-T2

4 (14%)

T3-T4

25 (86%)

pN

N0

9 (31%)

N1-N2

20 (69%)

Stage

I

3 (10%)

II

7 (24%)

III

19 (66%)

Grade

G1

4 (14%)

G2

19 (66%)

G3

6 (21%)

GX

0 (0%)

NAC

Neoadjuvant Chemotherapy

0 (0%)

Upfront Surgery

29 (100%)

ACT

Adjuvant Chemotherapy

19 (66%)

Observation

10 (34%)

MSI

MSS

26 (90%)

MSI-High

3 (10%)

BRAF.V600E

BRAF WT

26 (90%)

BRAF V600E

3 (10%)

RAS

RAS WT

17 (59%)

RAS Mut

12 (41%)

OS.months

22 (7 - 62)

1Median (Range); n (%)

save_as_docx(fit1, path= "~/Downloads/table1.docx")

#DFS by ctDNA at 12 months post-surgery

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.12months!="",]
circ_data$DFS.months=circ_data$DFS.months-12
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.12months, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.12months, data = circ_data)

                          n events median 0.95LCL 0.95UCL
ctDNA.12months=NEGATIVE 136      6     NA      NA      NA
ctDNA.12months=POSITIVE  14      7   6.69    3.74      NA
event_summary <- circ_data %>%
  group_by(ctDNA.12months) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.12months, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA at 12 months | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.12months, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.12months=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      31.0000       6.0000       0.9365       0.0263       0.8593       0.9720 

                ctDNA.12months=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        1.000        7.000        0.373        0.156        0.105        0.650 
circ_data$ctDNA.12months <- factor(circ_data$ctDNA.12months, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.12months, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.12months, data = circ_data)

  n= 150, number of events= 13 

                          coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.12monthsPOSITIVE  2.8501   17.2889   0.5708 4.993 5.94e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                       exp(coef) exp(-coef) lower .95 upper .95
ctDNA.12monthsPOSITIVE     17.29    0.05784     5.648     52.92

Concordance= 0.748  (se = 0.068 )
Likelihood ratio test= 20.99  on 1 df,   p=5e-06
Wald test            = 24.93  on 1 df,   p=6e-07
Score (logrank) test = 45.91  on 1 df,   p=1e-11
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 17.29 (5.65-52.92); p = 0"

#DFS by ctDNA 4-weeks post-adjuvant chemotherapy

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postACT!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.postACT, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.postACT, data = circ_data)

                         n events median 0.95LCL 0.95UCL
ctDNA.postACT=NEGATIVE 112     17     NA      NA      NA
ctDNA.postACT=POSITIVE   6      4   7.54    3.45      NA
event_summary <- circ_data %>%
  group_by(ctDNA.postACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA 4 weeks post-ACT | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.postACT, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.postACT=NEGATIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       42.000       14.000        0.845        0.039        0.749        0.906 

                ctDNA.postACT=POSITIVE 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI
circ_data$ctDNA.postACT <- factor(circ_data$ctDNA.postACT, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postACT, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.postACT, data = circ_data)

  n= 118, number of events= 21 

                         coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.postACTPOSITIVE  2.4960   12.1334   0.5794 4.308 1.65e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                      exp(coef) exp(-coef) lower .95 upper .95
ctDNA.postACTPOSITIVE     12.13    0.08242     3.897     37.77

Concordance= 0.612  (se = 0.051 )
Likelihood ratio test= 11.57  on 1 df,   p=7e-04
Wald test            = 18.56  on 1 df,   p=2e-05
Score (logrank) test = 30.09  on 1 df,   p=4e-08
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 12.13 (3.9-37.77); p = 0"

#DFS by ACT treatment in MRD negative - High Risk Stage II or Stage III

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ACT, data = circ_data)

            n events median 0.95LCL 0.95UCL
ACT=FALSE  93      5     NA      NA      NA
ACT=TRUE  187     23     NA      NA      NA
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95, 
    conf.type = "log-log")

                ACT=FALSE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      30.0000       5.0000       0.9186       0.0372       0.8054       0.9673 

                ACT=TRUE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       68.000       19.000        0.878        0.027        0.813        0.921 
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ACT, data = circ_data)

  n= 280, number of events= 28 

            coef exp(coef) se(coef)      z Pr(>|z|)
ACTFALSE -0.7813    0.4578   0.4935 -1.583    0.113

         exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE    0.4578      2.184     0.174     1.204

Concordance= 0.571  (se = 0.04 )
Likelihood ratio test= 2.93  on 1 df,   p=0.09
Wald test            = 2.51  on 1 df,   p=0.1
Score (logrank) test = 2.64  on 1 df,   p=0.1
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.46 (0.17-1.2); p = 0.113"
#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + MSI + Stage, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ ACT + Age.Group + Gender + MSI + 
    Stage, data = circ_data)

  n= 280, number of events= 28 

                coef exp(coef) se(coef)      z Pr(>|z|)  
ACTFALSE     -0.1808    0.8346   0.6123 -0.295   0.7678  
Age.Group≥70  0.3185    1.3750   0.4494  0.709   0.4785  
GenderMale    0.5723    1.7724   0.4009  1.428   0.1534  
MSIMSI-High   0.2271    1.2550   0.5780  0.393   0.6944  
StageIII      1.3172    3.7330   0.6395  2.060   0.0394 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

             exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE        0.8346     1.1982    0.2513     2.772
Age.Group≥70    1.3750     0.7273    0.5699     3.318
GenderMale      1.7724     0.5642    0.8078     3.889
MSIMSI-High     1.2550     0.7968    0.4042     3.896
StageIII        3.7330     0.2679    1.0658    13.075

Concordance= 0.668  (se = 0.055 )
Likelihood ratio test= 10.58  on 5 df,   p=0.06
Wald test            = 8.62  on 5 df,   p=0.1
Score (logrank) test = 9.59  on 5 df,   p=0.09
# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_fit)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)

#DFS by ACT treatment in MRD positive - High Risk Stage II or Stage III

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ACT, data = circ_data)

           n events median 0.95LCL 0.95UCL
ACT=FALSE 10      9   2.41    1.22      NA
ACT=TRUE  51     29  13.38   10.19      NA
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95, 
    conf.type = "log-log")

                ACT=FALSE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
    24.00000      1.00000      9.00000      0.10000      0.09487      0.00572      0.35813 

                ACT=TRUE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      11.0000      29.0000       0.3632       0.0744       0.2218       0.5060 
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ACT, data = circ_data)

  n= 61, number of events= 38 

           coef exp(coef) se(coef)     z Pr(>|z|)    
ACTFALSE 1.3968    4.0422   0.3909 3.573 0.000353 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

         exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE     4.042     0.2474     1.879     8.697

Concordance= 0.613  (se = 0.036 )
Likelihood ratio test= 9.94  on 1 df,   p=0.002
Wald test            = 12.77  on 1 df,   p=4e-04
Score (logrank) test = 14.86  on 1 df,   p=1e-04
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 4.04 (1.88-8.7); p = 0"
#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + Stage, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ ACT + Age.Group + Gender + Stage, 
    data = circ_data)

  n= 61, number of events= 38 

               coef exp(coef) se(coef)     z Pr(>|z|)    
ACTFALSE     1.9310    6.8967   0.4580 4.216 2.48e-05 ***
Age.Group≥70 0.5599    1.7505   0.3603 1.554   0.1202    
GenderMale   0.2086    1.2320   0.3471 0.601   0.5478    
StageIII     1.3374    3.8093   0.5942 2.251   0.0244 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

             exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE         6.897     0.1450    2.8106    16.923
Age.Group≥70     1.750     0.5713    0.8640     3.547
GenderMale       1.232     0.8117    0.6239     2.432
StageIII         3.809     0.2625    1.1887    12.208

Concordance= 0.669  (se = 0.044 )
Likelihood ratio test= 18.74  on 4 df,   p=9e-04
Wald test            = 20.61  on 4 df,   p=4e-04
Score (logrank) test = 22.94  on 4 df,   p=1e-04
# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_fit)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)

#DFS by ctDNA at the MRD Window & ACT - MSS Stable all stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.ACT, data = circ_data)

              n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 169     19     NA      NA      NA
ctDNA.ACT=2  48     27  13.07    9.69      NA
ctDNA.ACT=3 106      6     NA      NA      NA
ctDNA.ACT=4   3      2   7.42    5.29      NA
event_summary <- circ_data %>%
  group_by(ctDNA.ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.ACT=1 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       61.000       15.000        0.892        0.027        0.825        0.934 

                ctDNA.ACT=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       9.0000      27.0000       0.3640       0.0773       0.2174       0.5120 

                ctDNA.ACT=3 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       37.000        6.000        0.921        0.032        0.829        0.965 

                ctDNA.ACT=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
    24.00000      1.00000      2.00000      0.33333      0.27217      0.00896      0.77415 
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.ACT, data = circ_data)

  n= 326, number of events= 54 

                              coef exp(coef) se(coef)      z Pr(>|z|)    
ctDNA.ACTctDNA(+) & ACT     2.0237    7.5661   0.3021  6.700 2.09e-11 ***
ctDNA.ACTctDNA(-) & No ACT -0.6820    0.5056   0.4683 -1.456  0.14536    
ctDNA.ACTctDNA(+) & No ACT  2.1276    8.3950   0.7463  2.851  0.00436 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.ACTctDNA(+) & ACT       7.5661     0.1322    4.1856    13.677
ctDNA.ACTctDNA(-) & No ACT    0.5056     1.9778    0.2019     1.266
ctDNA.ACTctDNA(+) & No ACT    8.3950     0.1191    1.9443    36.247

Concordance= 0.759  (se = 0.034 )
Likelihood ratio test= 61  on 3 df,   p=4e-13
Wald test            = 65.55  on 3 df,   p=4e-14
Score (logrank) test = 98.87  on 3 df,   p=<2e-16
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.ACT, data = circ_data)

              n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 169     19     NA      NA      NA
ctDNA.ACT=2  48     27  13.07    9.69      NA
ctDNA.ACT=3 106      6     NA      NA      NA
ctDNA.ACT=4   3      2   7.42    5.29      NA
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.ACT=1 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       61.000       15.000        0.892        0.027        0.825        0.934 

                ctDNA.ACT=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       9.0000      27.0000       0.3640       0.0773       0.2174       0.5120 

                ctDNA.ACT=3 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       37.000        6.000        0.921        0.032        0.829        0.965 

                ctDNA.ACT=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
    24.00000      1.00000      2.00000      0.33333      0.27217      0.00896      0.77415 
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & No ACT","ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.ACT, data = circ_data)

  n= 326, number of events= 54 

                              coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.ACTctDNA(-) & ACT     0.6820    1.9778   0.4683 1.456 0.145361    
ctDNA.ACTctDNA(+) & ACT     2.7056   14.9638   0.4533 5.969 2.39e-09 ***
ctDNA.ACTctDNA(+) & No ACT  2.8096   16.6032   0.8192 3.430 0.000605 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.ACTctDNA(-) & ACT        1.978    0.50562    0.7898     4.953
ctDNA.ACTctDNA(+) & ACT       14.964    0.06683    6.1544    36.383
ctDNA.ACTctDNA(+) & No ACT    16.603    0.06023    3.3331    82.704

Concordance= 0.759  (se = 0.034 )
Likelihood ratio test= 61  on 3 df,   p=4e-13
Wald test            = 65.55  on 3 df,   p=4e-14
Score (logrank) test = 98.87  on 3 df,   p=<2e-16

#DFS by ctDNA at the MRD Window & ACT - MSI High all stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.ACT, data = circ_data)

             n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 19      4     NA      NA      NA
ctDNA.ACT=2  5      2     NA    3.09      NA
ctDNA.ACT=3 34      0     NA      NA      NA
ctDNA.ACT=4  6      5   1.91    1.41      NA
event_summary <- circ_data %>%
  group_by(ctDNA.ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")

summary(KM_curve, times= c(6, 24))
Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.ACT=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     15       3    0.839  0.0854        0.579        0.945
   24      3       1    0.774  0.1003        0.502        0.910

                ctDNA.ACT=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6      3       2      0.6   0.219        0.126        0.882
   24      2       0      0.6   0.219        0.126        0.882

                ctDNA.ACT=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     31       0        1       0           NA           NA
   24      9       0        1       0           NA           NA

                ctDNA.ACT=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     6.00000      1.00000      5.00000      0.16667      0.15215      0.00772      0.51680 
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500, maxstep = 1) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)
coxphf(formula = surv_object ~ ctDNA.ACT, data = circ_data, maxit = 500, 
    maxstep = 1)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood 

                               coef se(coef) exp(coef) lower 0.95 upper 0.95     Chisq            p
ctDNA.ACTctDNA(+) & ACT    2.813027 1.622557  16.66027   1.778749   2207.805  6.609234 1.014513e-02
ctDNA.ACTctDNA(-) & No ACT 3.712830 1.686348  40.96957   3.329543   5652.756  8.497597 3.556157e-03
ctDNA.ACTctDNA(+) & No ACT 4.942760 1.627985 140.15657  14.930527  18714.894 24.151928 8.902699e-07

Likelihood ratio test=26.64366 on 3 df, p=6.992091e-06, n=64
Wald test = 14.30441 on 3 df, p = 0.002518759

Covariance-Matrix:
                           ctDNA.ACTctDNA(+) & ACT ctDNA.ACTctDNA(-) & No ACT ctDNA.ACTctDNA(+) & No ACT
ctDNA.ACTctDNA(+) & ACT                   2.632691                   2.370004                   2.371709
ctDNA.ACTctDNA(-) & No ACT                2.370004                   2.843769                   2.373722
ctDNA.ACTctDNA(+) & No ACT                2.371709                   2.373722                   2.650336
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.ACT, data = circ_data)

             n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 19      4     NA      NA      NA
ctDNA.ACT=2  5      2     NA    3.09      NA
ctDNA.ACT=3 34      0     NA      NA      NA
ctDNA.ACT=4  6      5   1.91    1.41      NA
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")

summary(KM_curve, times= c(6))
Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.ACT=1 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      6.0000      15.0000       3.0000       0.8388       0.0854       0.5788       0.9451 

                ctDNA.ACT=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
       6.000        3.000        2.000        0.600        0.219        0.126        0.882 

                ctDNA.ACT=3 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
           6           31            0            1            0           NA           NA 

                ctDNA.ACT=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     6.00000      1.00000      5.00000      0.16667      0.15215      0.00772      0.51680 
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("2","4","1","3"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)
coxphf(formula = surv_object ~ ctDNA.ACT, data = circ_data, maxit = 500)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood 

                 coef  se(coef)  exp(coef)  lower 0.95 upper 0.95    Chisq           p
ctDNA.ACT4  1.2299304 0.8640952 3.42099155 0.771944824 20.3070261 2.600353 0.106839899
ctDNA.ACT1 -0.8998028 0.8581680 0.40664983 0.090118999  2.3336235 1.163885 0.280661276
ctDNA.ACT3 -3.7128298 1.6863479 0.02440835 0.000176905  0.3003415 8.497597 0.003556157

Likelihood ratio test=26.64366 on 3 df, p=6.992091e-06, n=64
Wald test = 14.30441 on 3 df, p = 0.002518759

Covariance-Matrix:
           ctDNA.ACT4 ctDNA.ACT1 ctDNA.ACT3
ctDNA.ACT4  0.7466605  0.4717519  0.4700468
ctDNA.ACT1  0.4717519  0.7364524  0.4737650
ctDNA.ACT3  0.4700468  0.4737650  2.8437691

#DFS by ctDNA Dynamics from MRD to Surveillance Window - all stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Dynamics, data = circ_data)

                   n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 263     10     NA      NA      NA
ctDNA.Dynamics=2  16      1     NA      NA      NA
ctDNA.Dynamics=3  25     11   28.4   15.77      NA
ctDNA.Dynamics=4  20     15   10.6    8.05      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Dynamics=1 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       93.000        8.000        0.961        0.014        0.922        0.981 

                ctDNA.Dynamics=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       8.0000       1.0000       0.9000       0.0949       0.4730       0.9853 

                ctDNA.Dynamics=3 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        6.000        9.000        0.519        0.125        0.260        0.727 

                ctDNA.Dynamics=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       2.0000      15.0000       0.2400       0.0980       0.0821       0.4428 
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)

  n= 324, number of events= 37 

                   coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.Dynamics2  0.5229    1.6869   1.0493 0.498    0.618    
ctDNA.Dynamics3  2.6136   13.6480   0.4383 5.963 2.48e-09 ***
ctDNA.Dynamics4  3.5381   34.4018   0.4141 8.545  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics2     1.687    0.59281    0.2157     13.19
ctDNA.Dynamics3    13.648    0.07327    5.7804     32.22
ctDNA.Dynamics4    34.402    0.02907   15.2803     77.45

Concordance= 0.822  (se = 0.04 )
Likelihood ratio test= 79.02  on 3 df,   p=<2e-16
Wald test            = 77.71  on 3 df,   p=<2e-16
Score (logrank) test = 168.8  on 3 df,   p=<2e-16
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Dynamics, data = circ_data)

                   n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 263     10     NA      NA      NA
ctDNA.Dynamics=2  16      1     NA      NA      NA
ctDNA.Dynamics=3  25     11   28.4   15.77      NA
ctDNA.Dynamics=4  20     15   10.6    8.05      NA
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Dynamics=1 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000       93.000        8.000        0.961        0.014        0.922        0.981 

                ctDNA.Dynamics=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       8.0000       1.0000       0.9000       0.0949       0.4730       0.9853 

                ctDNA.Dynamics=3 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        6.000        9.000        0.519        0.125        0.260        0.727 

                ctDNA.Dynamics=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       2.0000      15.0000       0.2400       0.0980       0.0821       0.4428 
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)

  n= 324, number of events= 37 

                    coef exp(coef) se(coef)      z Pr(>|z|)    
ctDNA.Dynamics4  0.92452   2.52065  0.39952  2.314   0.0207 *  
ctDNA.Dynamics1 -2.61359   0.07327  0.43834 -5.963 2.48e-09 ***
ctDNA.Dynamics2 -2.09071   0.12360  1.04642 -1.998   0.0457 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics4   2.52065     0.3967   1.15197     5.515
ctDNA.Dynamics1   0.07327    13.6480   0.03103     0.173
ctDNA.Dynamics2   0.12360     8.0907   0.01590     0.961

Concordance= 0.822  (se = 0.04 )
Likelihood ratio test= 79.02  on 3 df,   p=<2e-16
Wald test            = 77.71  on 3 df,   p=<2e-16
Score (logrank) test = 168.8  on 3 df,   p=<2e-16

#DFS by ctDNA Dynamics from MRD to Surveillance Window - High Risk Stage II or Stage III

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Dynamics, data = circ_data)

                   n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 224     10     NA      NA      NA
ctDNA.Dynamics=2  14      1     NA      NA      NA
ctDNA.Dynamics=3  22     10   28.4   16.03      NA
ctDNA.Dynamics=4  19     15   10.1    8.05      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Dynamics=1 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      77.0000       8.0000       0.9539       0.0166       0.9073       0.9774 

                ctDNA.Dynamics=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        7.000        1.000        0.889        0.105        0.433        0.984 

                ctDNA.Dynamics=3 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        6.000        8.000        0.542        0.128        0.272        0.750 

                ctDNA.Dynamics=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       2.0000      15.0000       0.1974       0.0948       0.0551       0.4032 
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)

  n= 279, number of events= 36 

                   coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.Dynamics2  0.4503    1.5688   1.0494 0.429    0.668    
ctDNA.Dynamics3  2.4212   11.2599   0.4483 5.401 6.64e-08 ***
ctDNA.Dynamics4  3.4366   31.0803   0.4147 8.286  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics2     1.569    0.63743    0.2006     12.27
ctDNA.Dynamics3    11.260    0.08881    4.6765     27.11
ctDNA.Dynamics4    31.080    0.03217   13.7872     70.06

Concordance= 0.816  (se = 0.04 )
Likelihood ratio test= 72.14  on 3 df,   p=1e-15
Wald test            = 72.35  on 3 df,   p=1e-15
Score (logrank) test = 150.9  on 3 df,   p=<2e-16
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~ 
    ctDNA.Dynamics, data = circ_data)

                   n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 224     10     NA      NA      NA
ctDNA.Dynamics=2  14      1     NA      NA      NA
ctDNA.Dynamics=3  22     10   28.4   16.03      NA
ctDNA.Dynamics=4  19     15   10.1    8.05      NA
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")

summary(KM_curve, times= c(24))
Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Dynamics=1 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000      77.0000       8.0000       0.9539       0.0166       0.9073       0.9774 

                ctDNA.Dynamics=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        7.000        1.000        0.889        0.105        0.433        0.984 

                ctDNA.Dynamics=3 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      24.000        6.000        8.000        0.542        0.128        0.272        0.750 

                ctDNA.Dynamics=4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     24.0000       2.0000      15.0000       0.1974       0.0948       0.0551       0.4032 
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)

  n= 279, number of events= 36 

                    coef exp(coef) se(coef)      z Pr(>|z|)    
ctDNA.Dynamics4  1.01533   2.76027  0.41237  2.462   0.0138 *  
ctDNA.Dynamics1 -2.42124   0.08881  0.44832 -5.401 6.64e-08 ***
ctDNA.Dynamics2 -1.97094   0.13933  1.05059 -1.876   0.0607 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics4   2.76027     0.3623   1.23012    6.1938
ctDNA.Dynamics1   0.08881    11.2599   0.03689    0.2138
ctDNA.Dynamics2   0.13933     7.1774   0.01777    1.0922

Concordance= 0.816  (se = 0.04 )
Likelihood ratio test= 72.14  on 3 df,   p=1e-15
Wald test            = 72.35  on 3 df,   p=1e-15
Score (logrank) test = 150.9  on 3 df,   p=<2e-16

#Median cfDNA concentration across different windows post-surgery

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
median_cfDNAconc <- circ_data %>%
  group_by(Timepoint) %>%
  dplyr::summarize(
    median_cfDNAconc = median(cfDNAconc, na.rm = TRUE),
    n_observations = n(),
    .groups = "drop"
  )
print(median_cfDNAconc)
boxplot(cfDNAconc~Timepoint, data=circ_data, main="median cfDNA Concentration", xlab="Intervals from Surgery", ylab="cfDNA Concentration", col="white",border="black", ylim = c(0, 40))

#Pairwise Wilcoxon-test p values
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)

# Initialize a matrix to store p-values for manual Wilcoxon tests
timepoints <- levels(circ_data$Timepoint)
p_value_matrix <- matrix(NA, nrow = length(timepoints), ncol = length(timepoints))
rownames(p_value_matrix) <- timepoints
colnames(p_value_matrix) <- timepoints

# Perform pairwise Wilcoxon tests manually and store p-values
for (i in 1:length(timepoints)) {
  for (j in i:length(timepoints)) {
    if (i != j) {
      # Subset data for the two Timepoints
      data1 <- circ_data %>% filter(Timepoint == timepoints[i]) %>% pull(cfDNAconc)
      data2 <- circ_data %>% filter(Timepoint == timepoints[j]) %>% pull(cfDNAconc)
      
      # Perform Wilcoxon test and store the p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Symmetric matrix
    } else {
      p_value_matrix[i, j] <- 1  # Set diagonal to 1 for clarity
    }
  }
}

# Replace NA values with 1 in the p-value matrix for completeness
p_value_matrix[is.na(p_value_matrix)] <- 1.00

# Melt the p-value matrix for plotting
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("Timepoint1", "Timepoint2", "p_value")

# Add asterisks based on p-value thresholds
p_value_data <- p_value_data %>%
  mutate(
    asterisk = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

# Create the heatmap with asterisks for significance levels
ggplot(p_value_data, aes(x = Timepoint1, y = Timepoint2, fill = p_value)) +
  geom_tile(color = "white") +  # Background tiles for grid
  geom_text(aes(label = asterisk), color = "black", vjust = -0.5) +  # Add asterisks
  scale_fill_gradient(low = "lightgreen", high = "red") +  # Gradient for p-values
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Pairwise Wilcoxon-Test P-Values",
       fill = "P-Value")

#ctDNA positivity across different windows post-surgery

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)

rate_by_timepoint <- circ_data %>%
  group_by(Timepoint) %>%
  summarise(
    n_total = n(),  # Total number of patients in each Timepoint
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
print(rate_by_timepoint)

# Create the stacked bar plot for positivity and negativity rates by Timepoint
bar_midpoints <- barplot(
  t(as.matrix(rate_by_timepoint[, c("percentage_positive", "percentage_negative")])),  # Transpose to get the correct format
  names.arg = rate_by_timepoint$Timepoint,
  col = c("red", "blue"),  # Colors: red for positive, blue for negative
  main = '% ctDNA Positive and Negative Samples by Timepoint',
  xlab = 'Timepoint',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_timepoint$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_timepoint$n_total + 3, labels = rate_by_timepoint$n_total, col = "black", cex = 0.8)


#Perform fisher's exact test matrix and heatmap
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
timepoints <- levels(circ_data$Timepoint)  # Use ordered levels
p_value_matrix <- matrix(NA, nrow = length(timepoints), ncol = length(timepoints))
rownames(p_value_matrix) <- timepoints
colnames(p_value_matrix) <- timepoints

for (i in 1:length(timepoints)) {
  for (j in i:length(timepoints)) {
    if (i != j) {
      # Subset data for the two Timepoints
      subset1 <- circ_data %>% filter(Timepoint == timepoints[i])
      subset2 <- circ_data %>% filter(Timepoint == timepoints[j])
      
      # Create contingency table
      contingency_table <- matrix(c(
        sum(subset1$biomarker_status == "POSITIVE"), sum(subset1$biomarker_status == "NEGATIVE"),
        sum(subset2$biomarker_status == "POSITIVE"), sum(subset2$biomarker_status == "NEGATIVE")
      ), nrow = 2, byrow = TRUE)
      
      # Perform Fisher's exact test
      test_result <- fisher.test(contingency_table)
      
      # Store p-value in the matrix
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Symmetric matrix
    } else {
      p_value_matrix[i, j] <- 1  # Set diagonal to 1 for clarity
    }
  }
}

print(p_value_matrix)
             0-2 weeks   2-4 weeks    4-6 weeks    6-8 weeks   8-10 weeks On-treatment Surveillance
0-2 weeks    1.0000000 1.000000000 6.919353e-01 2.857000e-01 0.6738454083 1.000000e+00 4.643687e-01
2-4 weeks    1.0000000 1.000000000 1.588616e-01 1.035825e-02 0.2594894746 1.000000e+00 2.706764e-03
4-6 weeks    0.6919353 0.158861559 1.000000e+00 1.913544e-01 1.0000000000 5.680924e-02 8.035880e-09
6-8 weeks    0.2857000 0.010358245 1.913544e-01 1.000000e+00 0.4193440867 2.126873e-03 1.724838e-09
8-10 weeks   0.6738454 0.259489475 1.000000e+00 4.193441e-01 1.0000000000 1.498403e-01 3.034774e-04
On-treatment 1.0000000 1.000000000 5.680924e-02 2.126873e-03 0.1498402610 1.000000e+00 6.108761e-08
Surveillance 0.4643687 0.002706764 8.035880e-09 1.724838e-09 0.0003034774 6.108761e-08 1.000000e+00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("Timepoint1", "Timepoint2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    asterisk = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

# Create the heatmap with asterisks for significance levels
ggplot(p_value_data, aes(x = Timepoint1, y = Timepoint2, fill = p_value)) +
  geom_tile(color = "white") +  # Background tiles for grid
  geom_text(aes(label = asterisk), color = "black", vjust = -0.5) +  # Add asterisks
  scale_fill_gradient(low = "lightgreen", high = "red") +  # Gradient for p-values
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Pairwise Fisher's Exact Test P-Values",
       x = "Timepoint 1",
       y = "Timepoint 2",
       fill = "P-Value")

#Logistic regression for ctDNA positivity

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]

# Calculate the 20th, 40th, 60th, 80th, and 100th percentiles for cfDNAconc
thresholds <- quantile(circ_data$cfDNAconc, probs = seq(0, 1, 0.2), na.rm = TRUE)
print(thresholds)
        0%        20%        40%        60%        80%       100% 
  1.125000   3.566842   4.764706   6.244382   9.357955 265.185000 
circ_data <- circ_data %>%
  mutate(cfDNAlevels = cut(cfDNAconc, 
                           breaks = thresholds, 
                           include.lowest = TRUE, 
                           labels = c("Q1", "Q2", "Q3", "Q4", "Q5")))

circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
circ_data$biomarker_status <- factor(circ_data$biomarker_status, levels = c("NEGATIVE", "POSITIVE"))
circ_data$cfDNAlevels <- factor(circ_data$cfDNAlevels, levels = c("Q3", "Q1", "Q2", "Q4", "Q5"), labels = c("4.76-6.24 ng/mL (q3)", "<3.57 ng/mL (q1)", "3.57-4.76 ng/mL (q2)", "6.24-9.36 ng/mL (q4)", ">9.36 ng/mL (q5)"))
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("4-6 weeks", "0-2 weeks", "2-4 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("I", "II", "III"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut"))
logistic_model <- glm(biomarker_status ~ cfDNAlevels + Timepoint + Stage + MSI + BRAF.V600E + RAS,
                      data = circ_data,
                      family = binomial)

# Extract odds ratios and confidence intervals
results <- tidy(logistic_model, conf.int = TRUE, exponentiate = TRUE) %>%
  mutate(term = factor(term, levels = term))  # Preserve term ordering
ggplot(results, aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
  geom_pointrange() +
  geom_hline(yintercept = 1, linetype = "dashed", color = "gray") +  # Reference line at OR = 1
  coord_flip() +
  labs(title = "Odds Ratios for Biomarker Status (Forest Plot)",
       x = "Predictor",
       y = "Odds Ratio (95% CI)") +
  theme_minimal()


cox_model <- coxph(Surv(rep(1, nrow(circ_data)), biomarker_status == "POSITIVE") ~ cfDNAlevels + Timepoint + Stage + MSI + BRAF.V600E + RAS, 
                   data = circ_data)
ggforest(cox_model, data = circ_data, main = "Odds Ratios for Biomarker Status", cpositions = c(0.02, 0.22, 0.4))


# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_model)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_model)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)
---
title: "Cohen et al_CLIA CRC_Clinical analysis 11022024"
output: html_notebook
---

library(swimplot)
library(pheatmap)
library(reshape2)
library(coxphf)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival)
library(broom)
library(survminer) 
library(ggplot2)
library(scales)
library(coxphf)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(parameters)
library(car)
library(ComplexHeatmap)
library(tidyverse)
library(readxl)
library(survival)
library(janitor)
library(openxlsx)
library(writexl)
library(rms)
library(DT)

#ctDNA Detection rate by Stage and Window
```{r}
#MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.Surveillance %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Anytime post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.anytime %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.anytime == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.anytime, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.anytime == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```

#ctDNA MRD Detection rate Stage I/II vs III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"))
circ_data$Stage_Grouped <- factor(ifelse(circ_data$Stage %in% c("I", "II"), "I/II", "III"))
contingency_table <- table(circ_data$Stage_Grouped, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(contingency_table)
print(chi_square_test)
```

#Demographics Table
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    pT,
    pN,
    Stage,
    Grade,
    NAC,
    ACT,
    MSI,
    BRAF.V600E,
    RAS,
    DFS.Event,
    OS.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon")),
    pT = factor(pT, levels = c("T0","T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    Stage = factor(Stage, levels = c("I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3","GX")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF WT", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS WT", "RAS Mut")),
    DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE,
  strip_md_bold = TRUE)
fit1
save_as_docx(fit1, path= "~/Downloads/table1.docx")
```


#Heatmap with Clinical & Genomics Factors
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)

ha <- HeatmapAnnotation(
  Stage = circ_data$Stage,
  Gender = circ_data$Gender,
  PrimSite = circ_data$PrimSite,
  pT = circ_data$pT,
  pN = circ_data$pN,
  Grade = circ_data$Grade,
  ACT = circ_data$ACT,
  MSI = circ_data$MSI,
  BRAF.V600E = circ_data$BRAF.V600E,
  RAS = circ_data$RAS,
  ctDNA.MRD = circ_data$ctDNA.MRD,
  ctDNA.Surveillance = circ_data$ctDNA.Surveillance,
  ctDNA.anytime = circ_data$ctDNA.anytime,
  DFS.Event = circ_data$DFS.Event,
  
  col = list(Stage = c("I" = "seagreen1", "II" = "orange", "III" = "purple"),
    Gender = c("Female" = "goldenrod" , "Male" = "blue4"),
    PrimSite = c("Right-sided colon" = "brown", "Left-sided colon" ="darkgreen"),
    pT = c("T0" = "khaki","T1-T2" = "khaki", "T3-T4" ="brown2"),
    pN = c("N0" = "cornflowerblue", "N1-N2" ="orange2"),
    Grade = c("GX" = "grey","G1" = "coral", "G2" ="darkgreen", "G3" = "yellow3"),
    ACT = c("TRUE" = "#C1211A", "FALSE" ="#008BCE"),
    MSI = c("MSS" = "grey", "MSI-High" ="black"),
    BRAF.V600E = c("WT" = "grey", "MUT" ="black"),
    RAS = c("WT" = "grey", "MUT" ="black"),
    ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.Surveillance = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.anytime = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    DFS.Event = c("TRUE" = "red3", "FALSE" ="blue")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()
```


#DFS by ctDNA at the MRD Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#ctDNA sample positive in the MRD Window - 2-10 week intervals
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$MRD.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
filtered_data <- circ_data %>%
  filter(ctDNA.MRD >= 2 & ctDNA.MRD <= 10) #intervals for 2-10 weeks
filtered_data$ctDNA_bucket <- cut(filtered_data$ctDNA.MRD, 
                                  breaks = c(2, 4, 6, 8, 10), 
                                  right = FALSE, 
                                  labels = c("2-4", "4-6", "6-8", "8-10"))
filtered_data <- filtered_data %>%
  filter(!is.na(ctDNA_bucket))
rate_by_bucket <- filtered_data %>%
  group_by(ctDNA_bucket) %>%
  summarise(
    n_total = n(),  # Total number of patients in the bucket
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
overall_stats <- filtered_data %>%
  summarise(
    total_samples = n(),
    total_positive = sum(biomarker_status == "POSITIVE"),
    total_negative = sum(biomarker_status == "NEGATIVE"),
    overall_percentage_positive = mean(biomarker_status == "POSITIVE") * 100
  )

combined_results <- bind_rows(rate_by_bucket, overall_stats)
print(combined_results)

# Create the stacked bar plot for positivity and negativity rates by bucket
bar_midpoints <- barplot(
  t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])),  # Transpose to get the correct format
  names.arg = rate_by_bucket$ctDNA_bucket,
  col = c("red", "blue"),  # Colors: red for positive, blue for negative
  main = '% ctDNA Positive and Negative Samples at the MRD Window',
  xlab = 'Weeks from Surgery',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$MRD.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
filtered_data <- circ_data %>%
  filter(ctDNA.MRD >= 0 & ctDNA.MRD <= 10) #intervals for 0-10 weeks
filtered_data$ctDNA_bucket <- cut(filtered_data$ctDNA.MRD, 
                                  breaks = c(0, 2, 4, 6, 8, 10), 
                                  right = FALSE, 
                                  labels = c("0-2","2-4", "4-6", "6-8", "8-10"))
filtered_data <- filtered_data %>%
  filter(!is.na(ctDNA_bucket))
rate_by_bucket <- filtered_data %>%
  group_by(ctDNA_bucket) %>%
  summarise(
    n_total = n(),  # Total number of patients in the bucket
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
overall_stats <- filtered_data %>%
  summarise(
    total_samples = n(),
    total_positive = sum(biomarker_status == "POSITIVE"),
    total_negative = sum(biomarker_status == "NEGATIVE"),
    overall_percentage_positive = mean(biomarker_status == "POSITIVE") * 100
  )

combined_results <- bind_rows(rate_by_bucket, overall_stats)
print(combined_results)

# Create the stacked bar plot for positivity and negativity rates by bucket
bar_midpoints <- barplot(
  t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])),  # Transpose to get the correct format
  names.arg = rate_by_bucket$ctDNA_bucket,
  col = c("red", "blue"),  # Colors: red for positive, blue for negative
  main = '% ctDNA Positive and Negative Samples at the MRD Window',
  xlab = 'Weeks from Surgery',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)
```

#Median number of timepoints in the MRD Window
```{r}
rm(list=ls())
setwd("~/Downloads")
filtered_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
filtered_data <- filtered_data[filtered_data$MRD.Window==TRUE,]
filtered_data <- filtered_data[!is.na(filtered_data$cfDNAconc), ]
filtered_data <- filtered_data %>%
  filter(ctDNA.MRD >= 2 & ctDNA.MRD <= 10) #intervals for 2-10 weeks

# Calculate the median number of ctDNA tests per patient
median_ctDNA_tests <- filtered_data %>%
  group_by(pts_id) %>%
  summarise(num_tests = n()) %>%
  summarise(median_tests = median(num_tests))
ctDNA_stats <- filtered_data %>%
  group_by(pts_id) %>%
  tally() %>%
  summarise(
    median_tests = median(n),
    min_tests = min(n),
    max_tests = max(n)
  )

print(median_ctDNA_tests)
print(ctDNA_stats)
```

#Multivariate cox regression for DFS at the MRD Window & Age threshold as 50 years - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group2 + PrimSite + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)

# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_fit)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)
```


#Univariate cox regression for factors used in MVA - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male")) #univariate for gender
cox_fit <- coxph(surv_object ~ Gender, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50")) #univariate for Age
cox_fit <- coxph(surv_object ~ Age.Group2, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon")) #univariate for Tumor Location
cox_fit <- coxph(surv_object ~ PrimSite, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4")) #univariate for Overall T Stage
cox_fit <- coxph(surv_object ~ pT, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2")) #univariate for Overall N Stage
cox_fit <- coxph(surv_object ~ pN, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High")) #univariate for MSI
cox_fit <- coxph(surv_object ~ MSI, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E")) #univariate for BRAF
cox_fit <- coxph(surv_object ~ BRAF.V600E, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut")) #univariate for RAS
cox_fit <- coxph(surv_object ~ RAS, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ctDNA at the MRD Window - Stage II
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ctDNA at the MRD Window - Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ctDNA at the MRD Window - Stage II & Risk Groups
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$Risk.Group!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.Risk = case_when(
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[!is.na(circ_data$ctDNA.Stage.II.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.Risk, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Stage.II.Risk) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.Risk, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage II Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Stage.II.Risk <- factor(circ_data$ctDNA.Stage.II.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxphf(surv_object ~ ctDNA.Stage.II.Risk, data=circ_data) 
summary(cox_fit)
```

#DFS by ctDNA at the MRD Window - Stage III & Risk Groups
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.III.Risk = case_when(
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[!is.na(circ_data$ctDNA.Stage.III.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Stage.III.Risk) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(18, 24))
circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.III.Risk = case_when(
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(18))
circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
```

#OS by ctDNA at the MRD Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#DFS by ctDNA at the Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#DFS by ctDNA at the Surveillance Window - Stages II
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#DFS by ctDNA at the Surveillance Window - Stages III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at the Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#ctDNA sample positive in the Surveillance Window - 6mo intervals
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$Surveillance.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
circ_data$ctDNA_bucket <- cut(circ_data$ctDNA.Surv, 
                              breaks = seq(0, max(circ_data$ctDNA.Surv, na.rm = TRUE), by = 6), 
                              right = FALSE, 
                              labels = paste0(seq(0, max(circ_data$ctDNA.Surv, na.rm = TRUE) - 6, by = 6), 
                                              "-", 
                                              seq(6, max(circ_data$ctDNA.Surv, na.rm = TRUE), by = 6)))

rate_by_bucket <- circ_data %>%
  group_by(ctDNA_bucket) %>%
  summarise(
    n_total = n(),  # Total number of patients in the bucket
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
overall_totals <- circ_data %>%
  summarise(
    ctDNA_bucket = "Total",  # Label for the total row
    n_total = n(),  # Total number of patients across all buckets
    n_positive = sum(biomarker_status == "POSITIVE"),  # Total number of positive cases across all buckets
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Overall positivity rate
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Total number of negative cases across all buckets
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Overall negativity rate
  )
positivity_rate_with_total <- bind_rows(rate_by_bucket, overall_totals)
print(positivity_rate_with_total)
bar_midpoints <- barplot(
  t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])),  # Transpose to get correct format
  names.arg = rate_by_bucket$ctDNA_bucket,
  col = c("red", "blue"),  # Red for positivity and blue for negativity
  main = '% ctDNA Positive and Negative Samples by 6-month Interval',
  xlab = 'Time from Start of Surveillance (Months)',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)
```

#Median number of timepoints in the Surveillance Window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$Surveillance.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]

# Calculate the median number of ctDNA tests per patient
ctDNA_stats <- circ_data %>%
  group_by(pts_id) %>%
  tally() %>%
  summarise(
    median_tests = median(n),
    min_tests = min(n),
    max_tests = max(n)
  )

# Print the result
print(ctDNA_stats)
```

#Time between imaging at the Surveillance window by ctDNA status
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC Imaging Frequency.csv")
tally(~ctDNA_Surveillance, data=circ_data, margins = TRUE)
median_AverageDateDifference <- aggregate(AverageDateDifference ~ ctDNA_Surveillance, data = circ_data, FUN = median)
print(median_AverageDateDifference)
circ_data$ctDNA_Surveillance <- factor(circ_data$ctDNA_Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative (n=151)","Positive (n=22)"))
boxplot(AverageDateDifference~ctDNA_Surveillance, data=circ_data, main="Average Time between Imaging at Surveillance Window", xlab="ctDNA at the Surveillance Window", ylab="Average Time Between Imaging (Days)", col="white",border="black")
m1<-wilcox.test(AverageDateDifference ~ ctDNA_Surveillance, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC Imaging Frequency.csv")
tally(~ctDNA_Surveillance, data=circ_data, margins = TRUE)
median_MedianDateDifference <- aggregate(MedianDateDifference ~ ctDNA_Surveillance, data = circ_data, FUN = median)
print(median_MedianDateDifference)
circ_data$ctDNA_Surveillance <- factor(circ_data$ctDNA_Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative (n=151)","Positive (n=22)"))
boxplot(MedianDateDifference~ctDNA_Surveillance, data=circ_data, main="Median Time between Imaging at Surveillance Window", xlab="ctDNA at the Surveillance Window", ylab="Median Time Between Imaging (Days)", col="white",border="black")
m2<-wilcox.test(MedianDateDifference ~ ctDNA_Surveillance, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)
```

#Time-dependent analysis in surveillance window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
```


#Time-dependent analysis in surveillance window - all stages ACT-treated
```{r}
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
```


#Time-dependent analysis in surveillance window - all stages Non-ACT-treated
```{r}
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="FALSE",]
dt_final <- dt_final[dt_final$tstart!="",]

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
```


#DFS by ctDNA subgroups at the Surveillance window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Surveillance.Groups!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~Surveillance.Groups, data = circ_data)
event_summary <- circ_data %>%
  group_by(Surveillance.Groups) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Surveillance.Groups <- factor(circ_data$Surveillance.Groups, levels=c("All Negative","Negative-Positive","All Positive"))
KM_curve <- survfit(surv_object ~ Surveillance.Groups, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","purple", "red"), title="DFS - ctDNA Subgroups Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("All Negative","Negative-Positive","All Positive"), legend.title="")
summary(KM_curve, times= c(24))
cox_fit <- coxph(surv_object ~ Surveillance.Groups, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Surveillance.Groups!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Surveillance.Groups <- factor(circ_data$Surveillance.Groups, levels=c("Negative-Positive","All Positive", "All Negative"))
cox_fit <- coxph(surv_object ~ Surveillance.Groups, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```

#Demographics table for ctDNA positive at the Surveillance window with no radiological recurrence
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance=="POSITIVE",]
circ_data <- circ_data[circ_data$DFS.Event==FALSE,]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    pT,
    pN,
    Stage,
    Grade,
    NAC,
    ACT,
    MSI,
    BRAF.V600E,
    RAS,
    OS.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon")),
    pT = factor(pT, levels = c("T0","T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    Stage = factor(Stage, levels = c("I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3","GX")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF WT", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS WT", "RAS Mut")),
    OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE,
  strip_md_bold = TRUE)
fit1
save_as_docx(fit1, path= "~/Downloads/table1.docx")
```

#DFS by ctDNA at 12 months post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.12months!="",]
circ_data$DFS.months=circ_data$DFS.months-12
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.12months, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.12months) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.12months, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA at 12 months | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.12months <- factor(circ_data$ctDNA.12months, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.12months, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ctDNA 4-weeks post-adjuvant chemotherapy
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postACT!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.postACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.postACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA 4 weeks post-ACT | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.postACT <- factor(circ_data$ctDNA.postACT, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postACT, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ACT treatment in MRD negative - High Risk Stage II or Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + MSI + Stage, data=circ_data)
summary(cox_fit)

# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_fit)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)
```


#DFS by ACT treatment in MRD positive - High Risk Stage II or Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + Stage, data=circ_data)
summary(cox_fit)

# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_fit)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)
```


#DFS by ctDNA at the MRD Window & ACT - MSS Stable all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & No ACT","ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data)
summary(cox_fit)
```


#DFS by ctDNA at the MRD Window & ACT - MSI High all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(6, 24))
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500, maxstep = 1) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(6))
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("2","4","1","3"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)
```


#DFS by ctDNA Dynamics from MRD to Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```


#DFS by ctDNA Dynamics from MRD to Surveillance Window - High Risk Stage II or Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```


#Median cfDNA concentration across different windows post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
median_cfDNAconc <- circ_data %>%
  group_by(Timepoint) %>%
  dplyr::summarize(
    median_cfDNAconc = median(cfDNAconc, na.rm = TRUE),
    n_observations = n(),
    .groups = "drop"
  )
print(median_cfDNAconc)
boxplot(cfDNAconc~Timepoint, data=circ_data, main="median cfDNA Concentration", xlab="Intervals from Surgery", ylab="cfDNA Concentration", col="white",border="black", ylim = c(0, 40))

#Pairwise Wilcoxon-test p values
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)

# Initialize a matrix to store p-values for manual Wilcoxon tests
timepoints <- levels(circ_data$Timepoint)
p_value_matrix <- matrix(NA, nrow = length(timepoints), ncol = length(timepoints))
rownames(p_value_matrix) <- timepoints
colnames(p_value_matrix) <- timepoints

# Perform pairwise Wilcoxon tests manually and store p-values
for (i in 1:length(timepoints)) {
  for (j in i:length(timepoints)) {
    if (i != j) {
      # Subset data for the two Timepoints
      data1 <- circ_data %>% filter(Timepoint == timepoints[i]) %>% pull(cfDNAconc)
      data2 <- circ_data %>% filter(Timepoint == timepoints[j]) %>% pull(cfDNAconc)
      
      # Perform Wilcoxon test and store the p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Symmetric matrix
    } else {
      p_value_matrix[i, j] <- 1  # Set diagonal to 1 for clarity
    }
  }
}

# Replace NA values with 1 in the p-value matrix for completeness
p_value_matrix[is.na(p_value_matrix)] <- 1.00

# Melt the p-value matrix for plotting
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("Timepoint1", "Timepoint2", "p_value")

# Add asterisks based on p-value thresholds
p_value_data <- p_value_data %>%
  mutate(
    asterisk = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

# Create the heatmap with asterisks for significance levels
ggplot(p_value_data, aes(x = Timepoint1, y = Timepoint2, fill = p_value)) +
  geom_tile(color = "white") +  # Background tiles for grid
  geom_text(aes(label = asterisk), color = "black", vjust = -0.5) +  # Add asterisks
  scale_fill_gradient(low = "lightgreen", high = "red") +  # Gradient for p-values
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Pairwise Wilcoxon-Test P-Values",
       fill = "P-Value")
```


#ctDNA positivity across different windows post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)

rate_by_timepoint <- circ_data %>%
  group_by(Timepoint) %>%
  summarise(
    n_total = n(),  # Total number of patients in each Timepoint
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
print(rate_by_timepoint)

# Create the stacked bar plot for positivity and negativity rates by Timepoint
bar_midpoints <- barplot(
  t(as.matrix(rate_by_timepoint[, c("percentage_positive", "percentage_negative")])),  # Transpose to get the correct format
  names.arg = rate_by_timepoint$Timepoint,
  col = c("red", "blue"),  # Colors: red for positive, blue for negative
  main = '% ctDNA Positive and Negative Samples by Timepoint',
  xlab = 'Timepoint',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_timepoint$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_timepoint$n_total + 3, labels = rate_by_timepoint$n_total, col = "black", cex = 0.8)

#Perform fisher's exact test matrix and heatmap
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
timepoints <- levels(circ_data$Timepoint)  # Use ordered levels
p_value_matrix <- matrix(NA, nrow = length(timepoints), ncol = length(timepoints))
rownames(p_value_matrix) <- timepoints
colnames(p_value_matrix) <- timepoints

for (i in 1:length(timepoints)) {
  for (j in i:length(timepoints)) {
    if (i != j) {
      # Subset data for the two Timepoints
      subset1 <- circ_data %>% filter(Timepoint == timepoints[i])
      subset2 <- circ_data %>% filter(Timepoint == timepoints[j])
      
      # Create contingency table
      contingency_table <- matrix(c(
        sum(subset1$biomarker_status == "POSITIVE"), sum(subset1$biomarker_status == "NEGATIVE"),
        sum(subset2$biomarker_status == "POSITIVE"), sum(subset2$biomarker_status == "NEGATIVE")
      ), nrow = 2, byrow = TRUE)
      
      # Perform Fisher's exact test
      test_result <- fisher.test(contingency_table)
      
      # Store p-value in the matrix
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Symmetric matrix
    } else {
      p_value_matrix[i, j] <- 1  # Set diagonal to 1 for clarity
    }
  }
}

print(p_value_matrix)
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("Timepoint1", "Timepoint2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    asterisk = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

# Create the heatmap with asterisks for significance levels
ggplot(p_value_data, aes(x = Timepoint1, y = Timepoint2, fill = p_value)) +
  geom_tile(color = "white") +  # Background tiles for grid
  geom_text(aes(label = asterisk), color = "black", vjust = -0.5) +  # Add asterisks
  scale_fill_gradient(low = "lightgreen", high = "red") +  # Gradient for p-values
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Pairwise Fisher's Exact Test P-Values",
       x = "Timepoint 1",
       y = "Timepoint 2",
       fill = "P-Value")
```


#Logistic regression for ctDNA positivity
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]

# Calculate the 20th, 40th, 60th, 80th, and 100th percentiles for cfDNAconc
thresholds <- quantile(circ_data$cfDNAconc, probs = seq(0, 1, 0.2), na.rm = TRUE)
print(thresholds)
circ_data <- circ_data %>%
  mutate(cfDNAlevels = cut(cfDNAconc, 
                           breaks = thresholds, 
                           include.lowest = TRUE, 
                           labels = c("Q1", "Q2", "Q3", "Q4", "Q5")))

circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
circ_data$biomarker_status <- factor(circ_data$biomarker_status, levels = c("NEGATIVE", "POSITIVE"))
circ_data$cfDNAlevels <- factor(circ_data$cfDNAlevels, levels = c("Q3", "Q1", "Q2", "Q4", "Q5"), labels = c("4.76-6.24 ng/mL (q3)", "<3.57 ng/mL (q1)", "3.57-4.76 ng/mL (q2)", "6.24-9.36 ng/mL (q4)", ">9.36 ng/mL (q5)"))
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("4-6 weeks", "0-2 weeks", "2-4 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("I", "II", "III"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut"))
logistic_model <- glm(biomarker_status ~ cfDNAlevels + Timepoint + Stage + MSI + BRAF.V600E + RAS,
                      data = circ_data,
                      family = binomial)

# Extract odds ratios and confidence intervals
results <- tidy(logistic_model, conf.int = TRUE, exponentiate = TRUE) %>%
  mutate(term = factor(term, levels = term))  # Preserve term ordering
ggplot(results, aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
  geom_pointrange() +
  geom_hline(yintercept = 1, linetype = "dashed", color = "gray") +  # Reference line at OR = 1
  coord_flip() +
  labs(title = "Odds Ratios for Biomarker Status (Forest Plot)",
       x = "Predictor",
       y = "Odds Ratio (95% CI)") +
  theme_minimal()

cox_model <- coxph(Surv(rep(1, nrow(circ_data)), biomarker_status == "POSITIVE") ~ cfDNAlevels + Timepoint + Stage + MSI + BRAF.V600E + RAS, 
                   data = circ_data)
ggforest(cox_model, data = circ_data, main = "Odds Ratios for Biomarker Status", cpositions = c(0.02, 0.22, 0.4))

# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_model)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_model)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)
```

